Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data

Soil Moisture Ocean Salinity satellite exploits the frequency of 1.4 gigahertz which represents the best conditions for salinity retrieval. The new challenge is to interpret the observed brightness temperature into the salinity. The main objective of this study is to measure the sea surface salinity...

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主要作者:	Abd. Rahim, Noorlida
格式:	Thesis
語言:	English
出版:	2014
主題:	G70.39-70.6 Remote sensing
在線閱讀:	http://eprints.utm.my/id/eprint/51409/25/NoorlidaAbdRahimMFGHT2014.pdf
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id	my-utm-ep.51409
record_format	uketd_dc
spelling	my-utm-ep.514092020-07-13T03:51:12Z Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data 2014-08 Abd. Rahim, Noorlida G70.39-70.6 Remote sensing Soil Moisture Ocean Salinity satellite exploits the frequency of 1.4 gigahertz which represents the best conditions for salinity retrieval. The new challenge is to interpret the observed brightness temperature into the salinity. The main objective of this study is to measure the sea surface salinity in the South China Sea using the Levenberg Marquardt algorithm. The methodology of this study involves the mapping of this algorithm to solve the non-linear least squares in order to obtain the salinity. The salinity was estimated based on the comparison between brightness temperature measured and brightness temperature simulated value of the successive iteration. The difference between both brightness temperature values is compared to the desired threshold at each iteration, this recursive process either updates the brightness temperature simulated or finally terminated if the brightness temperature difference is lower or higher than that threshold respectively. The salinity values estimated from the designed of Levenberg Marquardt algorithm tools were assembled, thus maps of sea surface salinity were produced. Some accuracy analyses were carried out to identify the appropriateness of a Levenberg Marquardt algorithm for the salinity retrieval. The results of the regression analysis and Pearson Correlation Coefficient indicate that sea surface salinity measured performs high correlation with the sea truth data, which is 0.9042 and ±0. 9509 psu, respectively. The analysis of variance by testing the hypothesis indicates that there is no substantial difference between the mean of sea surface salinity from the satellite and sea truth data. The root mean square error of measured sea surface salinity is smaller compared to the sea truth data values. In conclusion, the appropriateness of Levenberg Marquardt algorithm in inverting the salinity in the non-linear technique proved as a solution for ill-posed inversion that estimates the sea surface salinity from the Soil Moisture Ocean Salinity brightness temperature. 2014-08 Thesis http://eprints.utm.my/id/eprint/51409/ http://eprints.utm.my/id/eprint/51409/25/NoorlidaAbdRahimMFGHT2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:89334 masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformatiom and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.39-70.6 Remote sensing
spellingShingle	G70.39-70.6 Remote sensing Abd. Rahim, Noorlida Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data
description	Soil Moisture Ocean Salinity satellite exploits the frequency of 1.4 gigahertz which represents the best conditions for salinity retrieval. The new challenge is to interpret the observed brightness temperature into the salinity. The main objective of this study is to measure the sea surface salinity in the South China Sea using the Levenberg Marquardt algorithm. The methodology of this study involves the mapping of this algorithm to solve the non-linear least squares in order to obtain the salinity. The salinity was estimated based on the comparison between brightness temperature measured and brightness temperature simulated value of the successive iteration. The difference between both brightness temperature values is compared to the desired threshold at each iteration, this recursive process either updates the brightness temperature simulated or finally terminated if the brightness temperature difference is lower or higher than that threshold respectively. The salinity values estimated from the designed of Levenberg Marquardt algorithm tools were assembled, thus maps of sea surface salinity were produced. Some accuracy analyses were carried out to identify the appropriateness of a Levenberg Marquardt algorithm for the salinity retrieval. The results of the regression analysis and Pearson Correlation Coefficient indicate that sea surface salinity measured performs high correlation with the sea truth data, which is 0.9042 and ±0. 9509 psu, respectively. The analysis of variance by testing the hypothesis indicates that there is no substantial difference between the mean of sea surface salinity from the satellite and sea truth data. The root mean square error of measured sea surface salinity is smaller compared to the sea truth data values. In conclusion, the appropriateness of Levenberg Marquardt algorithm in inverting the salinity in the non-linear technique proved as a solution for ill-posed inversion that estimates the sea surface salinity from the Soil Moisture Ocean Salinity brightness temperature.
format	Thesis
qualification_level	Master's degree
author	Abd. Rahim, Noorlida
author_facet	Abd. Rahim, Noorlida
author_sort	Abd. Rahim, Noorlida
title	Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data
title_short	Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data
title_full	Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data
title_fullStr	Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data
title_full_unstemmed	Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data
title_sort	sea surface salinity retrieval based on levenberg marquardt algorithm using satellite data
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformatiom and Real Estate
publishDate	2014
url	http://eprints.utm.my/id/eprint/51409/25/NoorlidaAbdRahimMFGHT2014.pdf
_version_	1747817548385091584

Sea surface salinity retrieval based on Levenberg Marquardt algorithm using satellite data

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